Novel direct alpha spectroscopy technique for <sup>225</sup>Ac radiopharmaceutical detection in cancer cells
Bibliographic record
Abstract
Abstract Objective. Targeted Alpha Therapy (TAT) is a promising approach for treating metastatic cancers, utilizing alpha-emitting radionuclides conjugated to tumor-targeting molecules. Actinium-225 (225Ac) has emerged as a clinically relevant candidate due to its decay chain, which produces four successive alpha emissions, effectively damaging cancer cells. However, the nuclear recoil effect can lead to off-target redistribution of decay daughters, complicating dosimetry and increasing potential toxicity. This study aims to address these challenges by developing a direct alpha spectroscopy method for in vitro investigations of 225Ac radiopharmaceuticals. Approach. We developed the Bio-Sample Alpha Detector (BAD), a silicon-based detector designed to operate under ambient conditions, enabling direct alpha spectroscopy of cell samples. AR42J rat pancreatic tumor cells, which express somatostatin receptor 2 (SSTR2), were incubated with [225Ac]Ac-crown-TATE, [225Ac]Ac-PSMA-617, and [225Ac]Ac3+. The BAD setup allowed radiolabeled cell samples to be positioned within 100 µm of the detector for alpha spectra acquisition with statistical uncertainties of less than 1% in count rates. Geant4 Monte Carlo simulations were employed to validate the experimental results. Main results. Distinct spectral differences between radiolabeled cells and reference samples confirmed the uptake of [225Ac]Ac-crown-TATE by AR42J cells. Detection of 213Po, a decay daughter of 225Ac, indicated partial retention and release of decay products from cells, providing insight into intracellular retention and radionuclide redistribution. Geant4 simulations confirmed the alignment of experimental data with theoretical predictions. Significance. This study introduces a novel method for directly measuring the behavior of 225Ac and its decay daughters in biological samples using alpha spectroscopy. The BAD setup provides a valuable tool for investigating radionuclide retention, redistribution, and microdosimetry in radiopharmaceutical research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".